Integration of artificial intelligence and high-content screening enabled identification of drugs for long-term treatment of cerebral cavernous malformation disease
Abstract
BACKGROUND: Adults and children with cerebral cavernous malformations (CCMs) are at risk of experiencing lifelong complications such as hemorrhagic strokes, neurological deficits, and epileptic seizures. These complications can severely reduce quality of life. At present, there is no safe or effective therapeutic option for the long-term treatment of CCMs.
METHODS: Using advanced artificial intelligence (AI) and machine learning models, powered by the Benevolent Platform™, we aimed to identify therapeutic drug targets for CCM pathology (e.g., CCM1, CCM2, CCM3). An AI integrative approach utilized various data types from biomedical entities, including diseases, genes, tissues, and biological mechanisms, together with CCM transcriptomic experimental data. High-throughput drug screening of AI-selected FDA-approved medications, analyses of mitochondrial morphology, and studies on pharmacokinetics, pharmacodynamics, and toxicology were conducted in CCM animal models to identify drugs that could potentially be repurposed for the long-term treatment of CCM disease.
RESULTS: AI predicted the AMPK (AMP-activated protein kinase) and mTOR (mammalian target of rapamycin) pathways as potential therapeutic targets that contribute to CCM pathology. High-content screening validation revealed that the FDA-approved drug metformin, which acts as an AMPK agonist and mTOR inhibitor, can reverse changes in cell-cell junction organization and increase KLF4 expression, a marker for CCM, in human…
Read the paper
Related studies
- Metformin as an adjuvant in breast cancer treatment
- Metformin Lysosomal Targeting: A Novel Aspect to Be Investigated for Metformin Repurposing in Neurodegenerative Diseases?
- Aberrant HSF1 signaling activation underlies metformin amelioration of myocardial infarction in mice
- Metformin induces ZFP36 by mTORC1 inhibition in cervical cancer-derived cell lines
- Protein Interaction Network for Identifying Vascular Response of Metformin (Oral Antidiabetic)
- Preventing Long COVID With Metformin
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.